• Environmental Engineering Research
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• 제25권4호
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.158-165
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• 2018

Since a large amount of multimedia image has dramatically increased, it is important to search semantically relevant image. Thus, several semantic image annotation methods using RDF(Resource Description Framework) model in mobile environment are introduced. Earlier studies on annotating image semantically focused on both the image tag and the context-aware information such as temporal and spatial data. However, in order to fully express their semantics of image, we need more annotations which are described in RDF model. In this paper, we propose an annotation method inferencing with RDFS entailment rules and user defined rules. Our approach implemented in Moment system shows that it can more fully represent the semantics of image with more annotation triples.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.999-1000
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• 2017

본 논문에서는 이전의 의미 기반 이미지 어노테이션 및 검색 시스템 Moment(Mobile Semantic Image Annotation and Retrieval System)에 RDF(Resource Description Framework) 추론 기능을 사용한 어노테이션 방법을 제안한다. 이를 위하여 제안된 시스템은 Apache Jena Inference API를 통해 구현되였으며 각 이미지들이 가진 어노테이션의 개수가 증가되었다. 자동으로 추론된 결과 또한 SPARQL 질의를 통해 검색이 가능하며, 기존 어노테이션 결과에 대한 의미 검색을 더욱 효과적으로 할 수 있게 한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.313-314
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• 2008

Ad hoc network is a multi-hop wireless network formed with non-infrastructure. The fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.1-16
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• 2023

Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• 한국시뮬레이션학회논문지
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• 제20권4호
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• pp.139-147
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• 2011

전 세계적으로 자동차의 수요와 교통정보 서비스의 활용도가 높아지고 있다. 따라서 교통정보 서비스의 종류와 데이터의 양의 증가로 인해 많은 IT 자원 인프라가 필요하다. 인프라의 감소를 위해 클라우드 컴퓨팅이 주목을 받고 있으며, 자원관리를 위해 프로비저닝 스케줄링 기법이 필요하다. 본 논문에서는 클라우드 환경에서 교통정보 서비스를 위한 조건부 확률 추론을 활용한 프로비저닝 스케줄링(PSCPI: Provisioning Scheduling with Conditional Probability Inference)을 제안한다. PSCPI는 가상머신의 상태에 따라 나이브 베이즈 추론 기법을 사용하여 가상머신의 가용율에 따라 작업 할당을 할 수 있다. 나이브 베이즈 기반의 조건부 확률 추론 프로비저닝 스케줄링을 활용하여 교통정보 서비스에 높은 처리율과 활용율을 보인다.

• 마케팅과학연구
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• 제18권4호
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• pp.195-215
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• 2008

본 연구는 기업에 대한 소비자의 의도추론이 희소성 효과에 미치는 영향에 있어서 희소성 메시지의 수량한정 유형 및 폭의 조절효과를 파악하고자 진행되었다. 연구결과는 크게 두 가지로 요약할 수 있다. 첫째, 특별한정으로 제시되는 경우에는 소비자의 기업에 대한 의도추론은 희소성 효과에 영향을 미치지 않았다. 그러나 일반한정으로 제시되는 경우에는 소비자가 기업의 의도를 추론하는 경우가 그렇지 않은 경우에 비해 구매의도가 더욱 낮았다. 둘째, 수량한정의 폭이 작은 경우에는 소비자의 기업에 대한 의도추론은 희소성 효과에 영향을 미치지 않았다. 그러나 수량한정의 폭이 큰 경우에는 소비자가 기업의 의도를 추론하는 경우가 그렇지 않은 경우에 비해 구매의도가 더욱 낮았다.

• 자원ㆍ환경경제연구
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• 제11권1호
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• pp.99-119
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• 2002

As an alternative to classical maximum likelihood approach for analyzing dichotomous choice contingent valuation (DCCV) data, this paper develops a Bayesian approach. By using the idea of Gibbs sampling and data augmentation, the approach enables one to perform exact inference for DCCV models. A by-product from the approach is welfare measure, such as the mean willingness to pay, and its confidence interval, which can be used for policy analysis. The efficacy of the approach relative to the classical approach is discussed in the context of empirical DCCV studies. It is concluded that there appears to be considerable scope for the use of the Bayesian analysis in dealing with DCCV data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3671-3689
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• 2019

Software defined networking brings unique security risks such as control plane saturation attack while enhancing the performance of wireless sensor networks. The attack is a new type of distributed denial of service (DDoS) attack, which is easy to launch. However, it is difficult to detect and hard to defend. In response to this, the attack threat model is discussed firstly, and then a DDoS attack prevention extension, called FuzzyGuard, is proposed. In FuzzyGuard, a control network with both the protection of data flow and the convergence of attack flow is constructed in the data plane by using the idea of independent routing control flow. Then, the attack detection is implemented by fuzzy inference method to output the current security state of the network. Different probabilistic suppression modes are adopted subsequently to deal with the attack flow to cost-effectively reduce the impact of the attack on the network. The prototype is implemented on SDN-WISE and the simulation experiment is carried out. The evaluation results show that FuzzyGuard could effectively protect the normal forwarding of data flow in the attacked state and has a good defensive effect on the control plane saturation attack with lower resource requirements.